Paper Info
Title
Netdriller Version 2: A Powerful Social Network Analysis Tool
Abstract
Social network analysis has gained considerable attention since Web 2.0 emerged and provided the ground for two-ways interaction platforms. The immediate outcome is the availability of raw datasets which reflect social interactions between various entities. Indeed, social networking platforms and other communication devices are producing huge amounts of data which form valuable sources for knowledge discovery. Hence the need for automated tools like NetDriller capable of successfully maximizing the benefit from networked data. Most datasets which reflect kind of many to many relationship can be represented as a network which is a graph consisting of actors having relationships among each other. Many tools exist for network analysis inspired to extract knowledge from a constructed network. However, most of these tools require users to prepare as input a dataset that inspires the complete network which is then displayed and analyzed by the tool using the measures supported. A different perspective has been employed to develop NetDriller as a network construction and analysis tool which does some tasks beyond what is normally available in existing tools. NetDriller covers the lack that exists in other tools by constructing a network from raw data using data mining techniques. In this paper, we describe the second version of NetDriller which has been recently improved by adding new functions for a richer and more effective network construction and analysis. This keeps the tool up to date and with high potential to handle the huge volume of networks and the different types of raw data available for analysis.
Year
DOI
Venue
2018
10.1109/ICDMW.2018.00211
2018 18TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW)
Keywords
Field
DocType
Social Network Analysis, Data Mining, Network Construction, Link Prediction, Hierarchical Zooming
Data science,Graph,Data visualization,Social network,Computer science,Social network analysis,Raw data,Artificial intelligence,Knowledge extraction,Network analysis,Many-to-many (data model),Machine learning
Conference
ISSN
Citations 
PageRank 
2375-9232
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Salim Afra141.42
Tansel Özyer219623.30
Jon G. Rokne326345.63